Data Science for Beginners: Integration of Tableau with R/Python
As more and more organizations become alive to the possibilities brought about by data science, as explained in detail over at runrex.com, most of them are starting to concentrate on advanced analytics. Therefore, it has become important for them to consider how they can integrate their statistical findings into traditional BI tools. As is revealed in discussions on the same over at guttulus.com, many modern BI tools offer native interactions that may provide information to key decision-makers with more clarity and speed as compared to traditional, centralized options. This is where Tableau, which is an industry-leading BI/data visualization tool, comes in. this is because Tableau offers native integrations with common statistical tools like R and Python, enabling organizations with predictive analytics to inform decision making. This article will look to highlight what each of these technologies is about, followed by an outline of the integration process for R and Python with Tableau.
What is Tableau, R, and Python?
Before we get to the integration part, it is important to highlight what Tableau, R, and Python are all about.
- Tableau- Tableau, as covered over at runrex.com, is an industry leader in business intelligence, BI, and data visualization. It helps interactively create stunning visualizations and dashboards, and these visualizations help dig deeper into information to uncover trends and patterns.
- R- R is an open-source programming language that is dedicated to statistics and mathematics and is used for computation and visualization purposes. Its main purpose is in data analysis, machine learning, statistical modeling, and algorithms. Common features, as far as R is concerned include the fact that it is open-source as already mentioned, it provides all-in-one analysis and has a powerful package ecosystem. Some of the pros as far as it is concerned, include, the fact that is compatible with several other programming languages like Java, C++, C, Python, among others, it has amazing visualizations, it offers a range of packages, and it is platform-independent. It is used by several big organizations such as Microsoft, Twitter, Airbnb, among others.
- Python- As is covered in detail over at guttulus.com, Python is a general-purpose, open-source programming language. Its purpose is for data analysis, machine learning algorithms, and general scripting. Some of its main features include the fact that it is open-source just like R, it is extensible, broad, and has efficient libraries. Pros, as far as Python is concerned, include the fact that it has easy syntax, it is flexible, it has huge libraries, is a combination of object-oriented, structured, and functional programming, and so forth. It is also used by several big organizations such as Dropbox, Facebook, Google, and many others
What is Tableau integration?
Tableau, as highlighted in discussions on the same over at runrex.com, provides users with the ability to integrate with R and Python for more robust advanced analytics. With the help of this integration, users can create a calculated field invoking the R or Python engine to pass values out and return the output from that analysis. To receive and process these calls from Tableau, R and Python both require a go-between server. In this regard, as explained over at guttulus.com, R uses the Rserve application while Python uses TabPy. These can either run on your local machine or an actual server for multiple users and various operating system platforms. It is also worth pointing out that Rserve running on Windows can only handle one user connection at a time, and that Tableau can be set to use only one external service connection at a time. This is why it is easiest to focus on one or the other platform, even though changing the setting isn’t that difficult.
What are some of the best practices for integration?
According to the subject matter experts over at runrex.com, there are two major ways to integrate Python and R with Tableau: you can either call Python or R scripts to process or transform data as needed (ETL/ELT), or you can directly integrate via API calls to models. While both of these will work, it is important to point out that direct integration will decrease architectural complexity and increase the modularity of data science models, which means you can build a central model that can be used in several ways across your firm. This is why direct integration is preferred. Another advantage that comes with direct integration is the fact that it allows Tableau Professional to do most of the work, removing the need for multiple professions across the data stack.
Why should you integrate Tableau with R or Python?
Finally, we are going to finish by making a case for why you should consider integrating Tableau with R or Python. According to the folks over at guttulus.com, integrating Tableau with R or Python will allow your organization to enable predictive analytics on your roadmap to the top of the pyramid which will help create a single source of truth by visualizing the outputs of models that might be used elsewhere. Integrating Tableau with R or Python will also help you achieve the ultimate goal of data science, which is to augment the business users’ ability to make data-driven decisions, leading to better decisions, and ultimately increased revenue and profits.
As always, if you are looking for more information on this and other related topics, then the highly-rated runrex.com and guttulus.com have got you covered.